// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#ifndef EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H
#define EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H

namespace Eigen {

/** \class TensorGeneratorOp
  * \ingroup CXX11_Tensor_Module
  *
  * \brief Tensor generator class.
  *
  *
  */
namespace internal {
    template <typename Generator, typename XprType> struct traits<TensorGeneratorOp<Generator, XprType>> : public traits<XprType>
    {
        typedef typename XprType::Scalar Scalar;
        typedef traits<XprType> XprTraits;
        typedef typename XprTraits::StorageKind StorageKind;
        typedef typename XprTraits::Index Index;
        typedef typename XprType::Nested Nested;
        typedef typename remove_reference<Nested>::type _Nested;
        static const int NumDimensions = XprTraits::NumDimensions;
        static const int Layout = XprTraits::Layout;
        typedef typename XprTraits::PointerType PointerType;
    };

    template <typename Generator, typename XprType> struct eval<TensorGeneratorOp<Generator, XprType>, Eigen::Dense>
    {
        typedef const TensorGeneratorOp<Generator, XprType>& type;
    };

    template <typename Generator, typename XprType>
    struct nested<TensorGeneratorOp<Generator, XprType>, 1, typename eval<TensorGeneratorOp<Generator, XprType>>::type>
    {
        typedef TensorGeneratorOp<Generator, XprType> type;
    };

}  // end namespace internal

template <typename Generator, typename XprType> class TensorGeneratorOp : public TensorBase<TensorGeneratorOp<Generator, XprType>, ReadOnlyAccessors>
{
public:
    typedef typename Eigen::internal::traits<TensorGeneratorOp>::Scalar Scalar;
    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
    typedef typename XprType::CoeffReturnType CoeffReturnType;
    typedef typename Eigen::internal::nested<TensorGeneratorOp>::type Nested;
    typedef typename Eigen::internal::traits<TensorGeneratorOp>::StorageKind StorageKind;
    typedef typename Eigen::internal::traits<TensorGeneratorOp>::Index Index;

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorGeneratorOp(const XprType& expr, const Generator& generator) : m_xpr(expr), m_generator(generator) {}

    EIGEN_DEVICE_FUNC
    const Generator& generator() const { return m_generator; }

    EIGEN_DEVICE_FUNC
    const typename internal::remove_all<typename XprType::Nested>::type& expression() const { return m_xpr; }

protected:
    typename XprType::Nested m_xpr;
    const Generator m_generator;
};

// Eval as rvalue
template <typename Generator, typename ArgType, typename Device> struct TensorEvaluator<const TensorGeneratorOp<Generator, ArgType>, Device>
{
    typedef TensorGeneratorOp<Generator, ArgType> XprType;
    typedef typename XprType::Index Index;
    typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
    static const int NumDims = internal::array_size<Dimensions>::value;
    typedef typename XprType::Scalar Scalar;
    typedef typename XprType::CoeffReturnType CoeffReturnType;
    typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
    typedef StorageMemory<CoeffReturnType, Device> Storage;
    typedef typename Storage::Type EvaluatorPointerType;
    enum
    {
        IsAligned = false,
        PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
        BlockAccess = true,
        PreferBlockAccess = true,
        Layout = TensorEvaluator<ArgType, Device>::Layout,
        CoordAccess = false,  // to be implemented
        RawAccess = false
    };

    typedef internal::TensorIntDivisor<Index> IndexDivisor;

    //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
    typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
    typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;

    typedef typename internal::TensorMaterializedBlock<CoeffReturnType, NumDims, Layout, Index> TensorBlock;
    //===--------------------------------------------------------------------===//

    EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : m_device(device), m_generator(op.generator())
    {
        TensorEvaluator<ArgType, Device> argImpl(op.expression(), device);
        m_dimensions = argImpl.dimensions();

        if (static_cast<int>(Layout) == static_cast<int>(ColMajor))
        {
            m_strides[0] = 1;
            EIGEN_UNROLL_LOOP
            for (int i = 1; i < NumDims; ++i)
            {
                m_strides[i] = m_strides[i - 1] * m_dimensions[i - 1];
                if (m_strides[i] != 0)
                    m_fast_strides[i] = IndexDivisor(m_strides[i]);
            }
        }
        else
        {
            m_strides[NumDims - 1] = 1;
            EIGEN_UNROLL_LOOP
            for (int i = NumDims - 2; i >= 0; --i)
            {
                m_strides[i] = m_strides[i + 1] * m_dimensions[i + 1];
                if (m_strides[i] != 0)
                    m_fast_strides[i] = IndexDivisor(m_strides[i]);
            }
        }
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }

    EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) { return true; }
    EIGEN_STRONG_INLINE void cleanup() {}

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
    {
        array<Index, NumDims> coords;
        extract_coordinates(index, coords);
        return m_generator(coords);
    }

    template <int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
    {
        const int packetSize = PacketType<CoeffReturnType, Device>::size;
        EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
        eigen_assert(index + packetSize - 1 < dimensions().TotalSize());

        EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[packetSize];
        for (int i = 0; i < packetSize; ++i) { values[i] = coeff(index + i); }
        PacketReturnType rslt = internal::pload<PacketReturnType>(values);
        return rslt;
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const
    {
        const size_t target_size = m_device.firstLevelCacheSize();
        // TODO(ezhulenev): Generator should have a cost.
        return internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size);
    }

    struct BlockIteratorState
    {
        Index stride;
        Index span;
        Index size;
        Index count;
    };

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch, bool /*root_of_expr_ast*/ = false) const
    {
        static const bool is_col_major = static_cast<int>(Layout) == static_cast<int>(ColMajor);

        // Compute spatial coordinates for the first block element.
        array<Index, NumDims> coords;
        extract_coordinates(desc.offset(), coords);
        array<Index, NumDims> initial_coords = coords;

        // Offset in the output block buffer.
        Index offset = 0;

        // Initialize output block iterator state. Dimension in this array are
        // always in inner_most -> outer_most order (col major layout).
        array<BlockIteratorState, NumDims> it;
        for (int i = 0; i < NumDims; ++i)
        {
            const int dim = is_col_major ? i : NumDims - 1 - i;
            it[i].size = desc.dimension(dim);
            it[i].stride = i == 0 ? 1 : (it[i - 1].size * it[i - 1].stride);
            it[i].span = it[i].stride * (it[i].size - 1);
            it[i].count = 0;
        }
        eigen_assert(it[0].stride == 1);

        // Prepare storage for the materialized generator result.
        const typename TensorBlock::Storage block_storage = TensorBlock::prepareStorage(desc, scratch);

        CoeffReturnType* block_buffer = block_storage.data();

        static const int packet_size = PacketType<CoeffReturnType, Device>::size;

        static const int inner_dim = is_col_major ? 0 : NumDims - 1;
        const Index inner_dim_size = it[0].size;
        const Index inner_dim_vectorized = inner_dim_size - packet_size;

        while (it[NumDims - 1].count < it[NumDims - 1].size)
        {
            Index i = 0;
            // Generate data for the vectorized part of the inner-most dimension.
            for (; i <= inner_dim_vectorized; i += packet_size)
            {
                for (Index j = 0; j < packet_size; ++j)
                {
                    array<Index, NumDims> j_coords = coords;  // Break loop dependence.
                    j_coords[inner_dim] += j;
                    *(block_buffer + offset + i + j) = m_generator(j_coords);
                }
                coords[inner_dim] += packet_size;
            }
            // Finalize non-vectorized part of the inner-most dimension.
            for (; i < inner_dim_size; ++i)
            {
                *(block_buffer + offset + i) = m_generator(coords);
                coords[inner_dim]++;
            }
            coords[inner_dim] = initial_coords[inner_dim];

            // For the 1d tensor we need to generate only one inner-most dimension.
            if (NumDims == 1)
                break;

            // Update offset.
            for (i = 1; i < NumDims; ++i)
            {
                if (++it[i].count < it[i].size)
                {
                    offset += it[i].stride;
                    coords[is_col_major ? i : NumDims - 1 - i]++;
                    break;
                }
                if (i != NumDims - 1)
                    it[i].count = 0;
                coords[is_col_major ? i : NumDims - 1 - i] = initial_coords[is_col_major ? i : NumDims - 1 - i];
                offset -= it[i].span;
            }
        }

        return block_storage.AsTensorMaterializedBlock();
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool) const
    {
        // TODO(rmlarsen): This is just a placeholder. Define interface to make
        // generators return their cost.
        return TensorOpCost(0, 0, TensorOpCost::AddCost<Scalar>() + TensorOpCost::MulCost<Scalar>());
    }

    EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }

#ifdef EIGEN_USE_SYCL
    // binding placeholder accessors to a command group handler for SYCL
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler&) const {}
#endif

protected:
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void extract_coordinates(Index index, array<Index, NumDims>& coords) const
    {
        if (static_cast<int>(Layout) == static_cast<int>(ColMajor))
        {
            for (int i = NumDims - 1; i > 0; --i)
            {
                const Index idx = index / m_fast_strides[i];
                index -= idx * m_strides[i];
                coords[i] = idx;
            }
            coords[0] = index;
        }
        else
        {
            for (int i = 0; i < NumDims - 1; ++i)
            {
                const Index idx = index / m_fast_strides[i];
                index -= idx * m_strides[i];
                coords[i] = idx;
            }
            coords[NumDims - 1] = index;
        }
    }

    const Device EIGEN_DEVICE_REF m_device;
    Dimensions m_dimensions;
    array<Index, NumDims> m_strides;
    array<IndexDivisor, NumDims> m_fast_strides;
    Generator m_generator;
};

}  // end namespace Eigen

#endif  // EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H
